DESCRIPTION (adapted from the Abstract): In the last decade, there has been an explosion in information about biological structure-ranging from the structure of macromolecules to the structure of organs and organisms. The National Library of Medicine has targeted the representation, management, and manipulation of biological structure as a key element of its mission. Biological structure has special attributes that make computing with it challenging, including the variability in an individual structure over time, the range of structures existing within a population, and the variability in the degree of certainty with which we can determine biological structures. Since we use our knowledge of biological structure for a variety of critically important tasks (ranging from drug design to medical treatment planning), the representation of biological structure and of that structure's variation is a particularly challenging and important task. Taking advantage of the results of the previous grant period, we present a two-part plan for continuing our work on using probabilistic representations of structure to generate, modify, and analyze molecular structure. In the first part, we will study the information content of different sources of structural data (both experimental and theoretical), and the effects of this information on the quality of the computed structures. In the second part, we will develop and apply new methods for recognizing the functional features of uncertain molecular models, in order to bridge the gap between modeled structures and their use in predicting/understanding function. Finally, in addition to our controlled tests with synthetic data, we will also test our methods in three biological application areas. First, we will continue focused collaborations with crystallographers studying viral structure, seeking clues to aspects of their function with our site recognition code. Second, we will collaborate with a group studying filamentous proteins and nucleic acids to extend our site recognition code for these molecules. Third, we will test both our model estimation and function recognition capabilities on proteins encoded by the malaria genome. Malaria is an important cause of worldwide disease, and the proteins within its genome have, for the most part, not been the target of experimental structure determinations. Therefore, we will leverage other efforts in malaria genomics within our laboratory by attempting to estimate structures and assess their function.